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The Router node enables smart conditional routing in your workflows by directing data through different paths based on AI decisions or logical conditions.

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What Does the Router Node Do?

The Router node acts as a decision point in your workflow, splitting your flow into multiple paths and automatically choosing which path to follow. Think of it like an if-else statement, but significantly more powerful.

Up to 8 Routes

Create multiple conditional paths instead of just if/else logic

AI-Powered Decisions

Use intelligent routing based on content understanding

Logical Conditions

Apply precise rule-based routing with boolean operators

Automatic List Processing

Process batches with item-by-item evaluation
Router node directing support tickets to different paths
Real-World Example: Processing incoming support tickets
  • Route urgent tickets → Escalation team
  • Route billing questions → Finance department
  • Route general inquiries → Standard support queue
The Router evaluates each ticket and automatically sends it down the appropriate path.

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AI Routing vs Standard Routing

Choose the right routing mode based on whether your decisions require interpretation or can be defined with exact rules.

AI Routing

Use when you need content understandingBest for analyzing sentiment, understanding context, categorizing nuanced content, and making subjective classifications.Cost: 2-30 credits based on the AI model

Standard Routing

Use when you have clear criteriaBest for exact keyword matching, numerical comparisons, binary decisions, and deterministic routing.Cost: 0 credits (logic-based)

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Quick Comparison

AspectAI RoutingStandard Routing
Decision LogicContent interpretationKeyword/rule matching
ConsistencyIntelligent but may vary100% deterministic
SetupNatural language descriptionsLogical conditions
Best Use CaseNuanced categorizationClear yes/no criteria
  • AI Routing Details
  • Standard Routing Details

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When to Use AI Routing

Use AI routing when your routing decisions require content understanding and interpretation.Ideal scenarios:
  • Analyzing sentiment or tone
  • Understanding context and intent
  • Categorizing nuanced content
  • Making subjective classifications
Example Configuration:
Route 1: "Customer Complaint"
AI Condition: "when the email expresses dissatisfaction, 
               frustration, or requests a refund"

Route 2: "Product Inquiry"  
AI Condition: "when the email asks questions about features, 
               pricing, or product information"

Route 3: "Technical Support"
AI Condition: "when the email reports bugs, technical issues, 
               or requests help with setup"
AI routing is enabled by default. You can select different AI models based on complexity, with credit costs ranging from 2-30 credits per routing decision.

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Real-World Examples

  • AI Routing: Support Triage
  • Standard Routing: Lead Qualifier

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Support Request Triage

View Full Workflow →This workflow intelligently categorizes support requests using AI-powered routing.
1

Read Emails

Gmail Reader pulls support emails from a specific label
2

AI Routing

Router analyzes content to understand intent and urgency
3

Route to Teams

Each route triggers different actions for appropriate teams
Why AI Routing?
Support requests require interpretation - the AI understands context, tone, and intent to determine urgency and category. Keywords alone can’t capture this nuance.Router Setup:
  • Route 1: “General Questions” → Slack notification to general support
  • Route 2: “Billing Questions” → Email to finance team
  • Route 3: “Feature Request” → Create Linear ticket

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Loop Mode Processing

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How It Works

When you pass a list to the Router, each item is evaluated separately and can be routed to different paths based on its individual characteristics. Example: View Loop Mode Workflow → 
Input List: ["urgent ticket", "billing question", "general inquiry"]

Processing:
┌─────────────────────────┐
│ Item 1: "urgent ticket" │ → Routes to Support Team
├─────────────────────────┤
│ Item 2: "billing question" │ → Routes to Finance Team
├─────────────────────────┤
│ Item 3: "general inquiry" │ → Routes to General Team
└─────────────────────────┘

Result: Items distributed across different routes
This allows batch processing where each item in your list can take a different path based on its content.

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Configuration

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Required Inputs

The specific data field that determines which route to take. The Router analyzes this field to make routing decisions.Example for support tickets:
  • ticket_description - Route based on issue content
  • priority_level - Route based on urgency
  • customer_tier - Route based on customer status
The different paths your data can follow. Each route represents a distinct outcome or processing path.Example for support system:
  • Route 1: “Urgent” - High-priority issues
  • Route 2: “Billing Question” - Payment-related
  • Route 3: “Technical Support” - Product issues
  • Route 4: “General Inquiry” - Everything else

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Optional Settings

Toggle between AI-powered routing and standard logical routing.Default: Enabled (AI Mode)When to disable: When you need deterministic, rule-based routing with zero credit cost
Choose which AI model powers your routing decisions (only available in AI mode).Model tiers:
  • Standard models: 2 credits - Good for straightforward categorization
  • Advanced models: 20 credits - Better for complex decisions
  • Expert models: 30 credits - Best for nuanced interpretation

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Understanding Outputs

The Router creates separate output branches for each route. Each branch contains all the original inputs. Example Setup: 
Inputs to Router:
├── ticket_description (used for routing decision)
├── ticket_title
├── ticket_link
└── customer_email

Routes Created (ie. available outputs):
├── Urgent Branch → [All 4 inputs]
├── Billing Question Branch → [All 4 inputs]
└── Technical Support Branch → [All 4 inputs]

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Visual Flow

This design ensures downstream nodes have full context - you don’t lose related information when routing.

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Detailed Configuration Guide

  • AI Routing Setup
  • Standard Routing Setup

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Configuring AI Routing

Router node in AI mode configuration
1

Ensure AI Mode is Enabled

“Route With AI” should be toggled ON (this is the default)
2

Select Input to Evaluate

Choose which data field the AI should analyze for routing
3

Create Routes

Add up to 8 routes with clear, descriptive names
4

Write AI Conditions

Describe in natural language when each route should be used (optional but recommended)
5

Choose AI Model

Select model based on decision complexity

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Best Practices

Descriptive Route Names

✅ “Positive Review”, “Negative Review”
❌ “Route A”, “Route B”Even without conditions, AI infers logic from clear names

Clear AI Conditions

✅ “expresses anger, frustration, or demands action”
❌ “negative sentiment”Give AI enough context for accurate decisions

Right Model Selection

Match model power to complexity:
  • Simple categorization → Standard
  • Nuanced interpretation → Expert

Include Examples

Router examples

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Best Practices

  • General
  • AI Routing
  • Standard Routing

Start Simple

Begin with 2-3 routes, add complexity gradually as you test and validate

Test Thoroughly

Validate routing with diverse sample data including edge cases

Descriptive Naming

Clear route names improve debugging and maintenance significantly

Always Add Fallback

Include a catch-all route at the bottom to handle unmatched items

Order Matters

Place specific conditions first, general conditions last

Monitor Run Logs

Check logs regularly to verify routing behavior

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Troubleshooting

Problem: An item didn’t match any route conditions.Solutions:
  • Add a fallback route with opposite conditions to catch unmatched items
  • Review your conditions - may be too restrictive
  • Check input data for unexpected formats or values
Prevention:
  • Always include a catch-all route at the bottom
  • Test with diverse sample data including edge cases
Problem: Inputs being evaluated are different types (e.g., mixing text and lists).Solutions:
  • Ensure all inputs are the same type
  • Use data conversion nodes before the Router
  • Check upstream nodes for unexpected output types
Prevention:
  • Validate data types in testing
  • Use consistent data structures throughout workflow
More details on type mismatch →
Problem: List inputs have different lengths.Solutions:
  • Use Duplicate node to match list sizes
  • Filter lists to same length before routing
  • Verify upstream list generation
Prevention:
  • Check list lengths in testing
  • Ensure list-generating nodes produce consistent outputs
More details on list size mismatch →

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Debugging Checklist

Check Run Logs

Monitor which routes are being selected and verify expected behaviorView Run Logs Documentation →

Test Simple Cases First

Start with clear-cut examples before testing edge cases

Validate Input Data

Ensure data is clean, properly formatted, and matches expected types

Use Descriptive Names

Clear route names make debugging significantly easier

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